A hybridized intelligence model to improve the predictability level of strength index parameters of rocks

In the current paper, the uniaxial compressive strength (UCS) and Young modulus (E) of rocks were predicted using a hybridized intelligence method. The model was developed using an optimum multi-objective generalized feedforward neural network (GFFN) incorporated with an imperialist competitive metaheuristic algorithm (ICA) and managed using 208 datasets of different physical and mechanical quarries from almost all over of Iran. Rock class, density, porosity,P-wave velocity, point load index and water absorption were datacenter components. The predictability and accuracy performance of the hybridICA-GFFNmodel were discussed using different error criteria and confusion matrixes. The observed 5.4% and at least 32% improvement in hybridICA-GFFNthanGFFNand multivariate regression (MVR) demonstrated feasible and accurate enough tools that can effectively be applied for multi-objective prediction purposes. The influence of inputs on predicted outputs was also identified using two different sensitivity analyses.

Automated summary

The study presents a model for predicting the uniaxial compressive strength and Young modulus of rocks using a hybridized intelligence method, which shows potential accuracy and feasibility in multi-objective prediction tasks. Performance analysis of the model using different error criteria and confusion matrixes demonstrates improvements compared to traditional methods such as GFFN and MVR. Sensitivity analyses were conducted to identify the influence of input variables on predicted outputs.